Wildfire precursors show complementary predictability in different timescales

In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.


GENERAL COMMENTS
My overall appreciafion is that the study is very interesfing and has the advantage of being performed at a global scale, but the manuscript has too many flaws and needs a major revision before can be accepted for publicafion.The most important problems are lack of clarity and detail, especially in what concerns data and methods; vague, obvious, redundant, and unnecessary sentences; conceptual and methodological issues.Another problem with this manuscript is that it is not clear what is its contribufion to increasing knowledge about wildfire factors.The authors have to explain which of their results are new/innovafive, i.e., what are the noteworthy results?Next, I will address what seems to me to be the main problems with the study/manuscript and then list a set of specific quesfions/comments/suggesfions that help explain my overall appreciafion.Confidence in the results and conclusions strongly depends on the quanfity and quality of the data as well as the adequacy of the methodology.The descripfion of data and methodologies needs to be clarified and detailed.The reader does not always understand what the authors did, which prevents them from perceiving and validafing results and conclusions.In this regard, one important issue is the air temperature (Temp) precursor.The authors do not explain which temperature (minimum, mean, maximum, other) and at what level (surface,2 m,10 m,850 hPa,etc.).Based on the statement that "All the data sets are averaged or summed up to weekly…" maybe we can assume that Temp is some kind of average air temperature.However, it is also common knowledge that the vast majority of wildfires and burned areas occur under condifions more extreme than average.The authors somehow acknowledge this fact in the first paragraph of the "Main" secfion.Thus, in addifion to the authors having to explain which temperature they used, if they used the mean temperature, they will have to explain why.
The results of such a study strongly depend on the chosen precursors.A bad choice of precursors of a given type/group leads to equally bad results for that type of precursor.How can the reader be confident that the selected precursors are the best/most suitable?It is also not clear how the authors selected the precursors.Was there a pre-selecfion process?If yes, which one?How did they conclude that four of each type would be the proper number?If I understand correctly, this study has serious conceptual (please see specific comments) and methodological flaws that can decisively affect the results and lead the reader to wrong conclusions.One of the most important conceptual/methodological problems is the names and composifion of the precursors' groups.First, there are misclassified precursors.For example, the group of hydrological precursors does not seem to me to be the most suitable, as half of them are not hydrological, but atmospheric (precipitafion and FWI).See examples referred to in specific comments.Secondly, and as a consequence of the first, the groups do not seem to me to be the most suitable.For example, in the case of hydrological precursors, it seems to me that it would be more appropriate to consider a group of soil precursors or, possibly, soil moisture precursors.On the one hand, the state and type of vegetafion depend more on soil moisture than on all other hydrological aspects.I conclude that the authors agree with this statement because the group of hydrological precursors integrate measurements of soil moisture and not hydrology (e.g., surface and groundwater, with regard to their mechanical, physical and chemical properfies, their geographic distribufion, etc.).On the other hand, precursors should be independent within each group and between groups, which they clearly are not.The variables are all related to each other in such an obvious way that it is not even necessary to menfion an example.The wildfire research community recognizes and agrees that, for a vegetafion wildfire to occur, the following requirements are necessary: (i) fuels/plant biomass, (ii) atmospheric condifions that lead the fuels/vegetafion to an adequate state of dryness and, (iii) a source of ignifion.The adequate state of dryness of the fuels (live or dead vegetafion) is reached with atmospheric condifions of high temperature and low humidity that lead the vegetafion to hydric and/or thermal stress for some fime (depending on the type and state of the vegetafion).Despite this common knowledge, the authors decided to reclassify atmospheric factors into atmospheric and hydrological.They consider: measures of energy (temperature, radiafion and evaporafion) and call them atmospheric precursors; and, measures of water availability (precipitafion, air and soil humidity), which are essenfially atmospheric and call them hydrological precursors.It is not possible to conclude that the hydrologic precursors are more or less important than the others if the hydrologic precursors are not hydrologic.The authors also do not describe what might be the limitafions and constraints that could affect the conclusions of the study.Without a major revision of the manuscript to clarify and correct the problems idenfified in this general appreciafion and in the specific comments, it is not worthwhile to analyse other aspects of the manuscript.

SPECIFIC COMMENTS
Line 4: In this manuscript, the authors used the concept of fire (a planned, wanted and controlled combusfion), which I believe is not correct; the authors should use the concept of wildfire (an unplanned and unwanted combusfion of dead or alive vegetal biomass in a rural or natural area).Line 4: the first sentence is completely vague; what role?What services?How? Line 30: Is it really possible to "predict" wildfires "reliably and accurately Lines 31-32: "Fuel availability, usually in the form of vegetafion condifions,…", fuel availability is not equal to vegetafion condifions.Please correct the sentence.Line 36: I agree that the FWI system is used widely.However, since it was developed for Canada, it must be calibrated before can be used in any other locafion.How did you calibrate the FWI indices in order to be able to use them globally?Line 46: I suppose that the black circle character is a typo/not necessary.Line 46: you menfioned that you use air temperature (Temp).However, you do not explain what air temperature is; the minimum, mean, maximum or other air temperature?Air temperature at what level?Line 48: can you explain why did you classify Precipitafion, which can be defined as the fall of water, in any form (rain, snow, hail, hail, etc.), from the atmosphere to the earth's surface, as a hydrologic and not an atmospheric precursor?Lines 48-49: Please explain why you classify the fire WEATHER index (FWI) as a hydrologic and not an atmospheric precursor.Please note that the Canadian Forest Fire Weather Index (FWI) System consists of six components that account for the effects of fuel moisture and WEATHER condifions on fire behaviour, computed with noon air temperature, air relafive humidity and wind speed, and 24-hour accumulated precipitafion.Please quanfify and explain how these misclassificafions impact your findings.Line 57 (Fig. 1a): Please explain why the sum of the parfial correlafions of all 12 fire precursors per ecoregion ranges between 0 and 2. Lines 57-61: "For all the 232 ecoregions, the dominant area fracfion of the precursor (Fig. S1) shows that VPD (47.6%) is the most important atmospheric precursor followed by Rad,Temp,and ET0 (23.7%,18.1%,10.6%, respecfively)".What is the meaning of these results?Does this mean that VPD is the most important precursor in 47.6% of the 232 ecoregions, analysed independently or jointly?If I understand correctly, the sum of the percentages of the four precursors of each type is 100%.Does this mean that you apply PCMCI independently for the precursors of each type/group (for all the ecoregions)?Please clarify.Lines 72-73: How did you compute these percentages?Did you use the number of ecoregions, the area, or what?Line 83 and 91: this is only due to the precursors you selected and how you classify them.Line 87: another black circle!Lines 87-89: An obvious and unnecessary sentence.Is there any region where the atmospheric precursors do not play an important role?Line1 91-92: "Fires tend to be negafively related to hydrologic precursors (72.6%), and less frequently this relafionship is posifive (27.4%), see Fig. 1d"; the part ", and less frequently this relafionship is posifive (27.4%)," of the sentence is completely unnecessary; the reader can easily compute 100-72.6=27.4.Lines 92-94: another obvious and unnecessary sentence; it is obvious that drought and precipitafion deficits may provide opfimal condifions for wildfires while the opposite, a "high water supply" can prevent fires.Lines 94-96: What "dominant posifive relafionship" Line 110: I am confused.If "All the data sets are averaged or summed up to weekly" (line 287), how can you have assessed the parfial correlafion for fime lags shorter than 1 week?Lines 112-113: "…, which means in such regions fires are ignited shortly after (within a few days) the changes in the fire precursors."another obvious and unnecessary sentence.Apparently, you used monthly fire data can the reader assume that the fime series of all other variables are also monthly?If not (line 287), how did you proceed to analyse data with different temporal sampling?Datasets do not have the same spafial resolufion; how did you process the data with different spafial resolufions?Lines 262-263.Why did you use temperature, radiafion, and precipitafion of the Global Land Data Assimilafion System (GLDAS) but ET0, VPD, FWI and AAI from ERA5?Why did you not use all weather data from the same source, for example, ERA5, which provides you with not only global but also consistent (homogeneous) data?Lines 271-285: the reader may not have access to all publicafions cited in the manuscript; this is why the methodology must be, eventually briefly but clearly described, which is not the case.In addifion, the descripfion of how AAI was computed is unclear; for example, "For each year, we calculated the difference between AI at a certain period and the normal AI for the corresponding period to calculate the anomaly, then the AAI was obtained."Lines 286-305 (Data Processing Procedures): you menfioned that you use monthly fire data (line 256 "…It is a global and monthly BA product") and "All the data sets are averaged or summed up to weekly values…".Please explain this apparent contradicfion.So, you produce weekly fime series for each of the 28 climate zones (from Köppen-Geiger) and 8 land cover classes (from ESA CCI).The quesfions are: What was the original temporal resolufion of the downloaded data?How did you proceed to obtain the fime series for each of the 232 ecoregions 28 (or 29) climate zones and 8 vegetafion types?Line 294: Please clarify what is a "… PC condifion selecfion …"; all acronyms must be clearly defined.Lines 294-295: Please clarify what is "…an MCI condifional independence test …"; all acronyms must be clearly defined.Line 295: "In the first stage, irrelevant condifions are removed"; what is an irrelevant condifion?Lines 296-298: "Two main assumpfions of PCMCI are fime-series stafionarity and causal sufficiency.The first assumpfion can be safisfied by calculafing anomalies and de-trending the fime series."Once again, the authors do not adequately explain what leads to doubt.First, obviously, it is not by calculafing anomalies that the series becomes stafionary; just think that the trend does not disappear.Second, you don't explain what trend you remove and how.Did you just remove the trend over the enfire study period?Did you check and correct the seasonality?This whole procedure must be properly explained.
This study focused on identifying the most important local predictors of fires.The authors provided comprehensive analysis on multiple aspects of the impacts of fire precursors.I find the results interesting and helpful.However, several issues should be addressed.I have listed my comments below.

Response:
We appreciate the constructive comments and suggestions provided by the reviewer to improve the quality of our manuscript.Our responses to the reviewer's major and specific comments are given below.

Major comment #1
• This work focused on the impacts of local predictors on fires.However, the method is partly unclear.For example, for a given pixel with fire data, is the data of predictors also obtained from the same pixel or surroundings?As fires may not be limited in one pixel, the selected approach may affect the results and conclusions.

Response:
We appreciate the reviewer's careful review and revised the Methods part to clarify how we processed the data.Since not the entire Earth surface has experienced wildfires in the past decades, we performed the analysis based on 232 ecoregions.We, therefore, implemented the following approach: We first resampled all the data to a spatial resolution of 0.25° and calculated the weekly mean (wildfire precursors) or sum (burned area) values.Then, we calculated the ecoregion mean or sum values by averaging or summing up all the pixels located in the ecoregions, i.e., the following analysis is purely based on the ecoregion level, not the pixel level.The revised Methods are as follows: The wildfire indicator used in this study is the BA taken from FireCCI51 73 .FireCCI51 is based on spectral and thermal information from the Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) band and active wildfire products.It is a global and monthly BA product with two different spatial resolutions (250m of pixel level and 0.25° of grid level).Here we used the pixel-level data.The main information is included in three layers, i.e., the first fire detection day, the confidence level, and its corresponding land cover.A quality control process was applied to mask low-quality BA pixels whose confidence level is lower than 70%.A logarithmic transformation was applied to transform skewed BA data to a normal Gaussian distribution to make the detected causal relationship reliable 74, 75 .

, and aridity anomaly index (AAI). The bottom-up precursors include the fraction of photosynthetically active radiation (FPAR), gross primary production (GPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and soil water deficit index (SWDI). A detailed description of the precursors -including
which products were used, their spatial and temporal resolution, how they were calculated, and how these precursors were selected -can be found in the Supplementary Materials.
All the above-mentioned data sets span from 2003 to 2020.Since not the entire world surface has experienced wildfires in the past decades, we performed the analysis based on ecoregions instead of at a pixel level.In each of the 232 ecoregions (29 climate zones and 8 land cover classes), BA and precursors were resampled to a spatial resolution of 0.25° and averaged (precursors) or summed up (BA) to weekly values.Then, we calculated the ecoregional mean (precursors) or sum (BA) values by averaging or summing up all the pixels located in the ecoregions.Note that even though FireCCI51 is a monthly dataset, it includes a layer recording the first fire detection day.Therefore, we computed the weekly summed BA from this daily information.For the precursors, we calculated the weekly mean from their original temporal scale.
The climate zone data used in this study is the global digital Köppen-Geiger map 76 from the University of East Anglia and the German Weather Service representing the climate classification of the period from 1986 to 2010 at the spatial resolution of 5 arc minutes.The vegetation type used in this study is from the land cover layer of FireCCI51.(Line 286-313)

Major comment #2
• ENSO is a single useful predictor of fires in many regions (e.g., North America, the tropics, Australia (Fang et al., 2021;Fuller & Murphy, 2006)).In fact, ENSO predictability is achievable (Ham et al., 2019) and the variations of most of fire precursors are driven by ENSO (Le & Bae, 2022;Thirumalai et al., 2017).Hence, it might be too complicated to use multiple fire precursors as suggested by this work.

Response:
We appreciate the reviewer's insights about the dominance of major climate modes, like ENSO, on wildfires, and we agree.Part of our author team is involved in an ongoing study, in which the influence of major climate modes on burned area is investigated.Below are all the climate mode indices we included in the ongoing study:
Response: Thanks for the reviewer's constructive suggestion.We addressed the role of major climate modes on wildfires as follows: Wildfires are positively related to top-down precursors globally (91.6% positive, Fig. 1d).This positive relationship indicates that in flammability-limited regions, BA will increase when more energy is supplied (higher Tmax and ET0) or the environment is drier (higher VPD and AAI).This is supported by evidence indicating that wildfires increase with the increased frequency and intensity of summer heatwaves 25 , the strengthened solar radiation absorption 26, 27 , and the increased atmospheric water demand and drought conditions 28, 29 (see Fig. S2 for ET0, AAI, and VPD dominance).Major climate modes (e.g., El Niño-Southern Oscillation, and North Atlantic Oscillation) also modulate the occurrence of these weather anomalies 30, 31, 32, 33, 34 and as such play an important role in governing global patterns of wildfire activities 33, 35, 36 .The top-down dominance in the rainforest is supported by studies showing that here wildfires are limited to the time window when fuels are dry enough to burn 37, 38 , see Fig. S2

Minor comment #2
• Lines 225-251: The role of major climate modes should be discussed.For example, ENSO (McPhaden et al., 2006) and NAO (Hurrell et al., 2003) modulate the variations of multiple fire predictors and thus may affect regional fires.

Response:
We appreciate the reviewer's constructive comment.As explained in our response to Major comment #2, we find that our study on precursors influences global fire activity complementary to earlier work that investigated relationships between major climate modes and fire activity.In our revision, we now better connect our work with these earlier studies and discuss as a limitation the non-explicit consideration of the influence of climate modes: There are three limitations of this study that could be addressed further.First, although benefiting from the big data era, the number of precursors included in this study is still limited.Major climate modes (e.g., El Niño-Southern Oscillation, and North Atlantic Oscillation) modulate regional top-down and bottom-up conditions and dominant wildfires 69, 70 .Therefore, wildfire prediction could benefit from the achievable predictability of El Niño-Southern Oscillation 71 and other major climate modes.Human behavior (e.g., ignition, cropland expansion, and fire suppression) is an important wildfire impact factor that may alter causal relationships.However, limited by the availability of high temporal resolution climate mode and human behavior indices, we currently cannot conduct such an analysis.Second, a better wildfire precursor grouping system may be beneficial.While we divided the precursors into top-down and bottom-up groups which are conceptually clear 72 , sometimes, the top-down precursors dominate wildfires through their influence on fuel accumulation that is deduced from their long time lags, making the causal inference complex.Third, we assumed the causal relationship to be constant throughout the whole study period.

REVIEWER REPORT SUMMARY
In this study, the authors used a causal inference approach (Peter & Clark Momentary Conditional Independence, PCMCI) to evaluate the influence of atmospheric, hydrologic, and vegetation precursors on fires and their time lags.

GENERAL COMMENT #1:
My overall appreciation is that the study is very interesting and has the advantage of being performed at a global scale, but the manuscript has too many flaws and needs a major revision before can be accepted for publication.The most important problems are lack of clarity and detail, especially in what concerns data and methods; vague, obvious, redundant, and unnecessary sentences; conceptual and methodological issues.Another problem with this manuscript is that it is not clear what is its contribution to increasing knowledge about wildfire factors.The authors have to explain which of their results are new/innovative, i.e., what are the noteworthy results?
Next, I will address what seems to me to be the main problems with the study/manuscript and then list a set of specific questions/comments/suggestions that help explain my overall appreciation.

Response:
We appreciate the reviewer's positive comment on this topic and regret our unclarity with the data, methods, and significance.We believe that our manuscript now has been improved significantly according to the reviewer's comments and suggestions.Our responses to the reviewer's general and specific comments are given below.
We also highlighted the noteworthy results in the Implications section: In conclusion, this is the first globally consistent study partitioning top-down weather and bottom-up fuel precursors of wildfire activity.On a global scale, top-down weather precursors dominate in 73.3% of regions where causal relationships are detected.In the remaining 26.7% of regions, bottom-up fuel precursors are dominant, which coincides with North American and European boreal forests, and African and Australian savannahs.In these regions, fuel management practices may be the most efficient.(Line 267-271)

GENERAL COMMENT #2:
Confidence in the results and conclusions strongly depends on the quantity and quality of the data as well as the adequacy of the methodology.The description of data and methodologies needs to be clarified and detailed.The reader does not always understand what the authors did, which prevents them from perceiving and validating results and conclusions.In this regard, one important issue is the air temperature (Temp) precursor.The authors do not explain which temperature (minimum, mean, maximum, other) and at what level (surface, 2 m, 10 m, 850 hPa, etc.).Based on the statement that "All the data sets are averaged or summed up to weekly…" maybe we can assume that Temp is some kind of average air temperature.However, it is also common knowledge that the vast majority of wildfires and burned areas occur under conditions more extreme than average.The authors somehow acknowledge this fact in the first paragraph of the "Main" section.Thus, in addition to the authors having to explain which temperature they used, if they used the mean temperature, they will have to explain why.

Response:
We appreciate the reviewer's constructive comment to clarify the Data section.We used daily mean 2m air temperature in the earlier submitted version.Thanks for the reviewer's suggestion about the maximum temperature.We agree that wildfires are more related to the maximum temperature.So, in this revised version, we replaced the daily mean 2m air temperature with the daily maximum 2m air temperature.To make clear which variables/precursors were used in this study, we summarized this information in a new Supplementary Table (Table S1):

GENERAL COMMENT #3:
The results of such a study strongly depend on the chosen precursors.A bad choice of precursors of a given type/group leads to equally bad results for that type of precursor.How can the reader be confident that the selected precursors are the best/most suitable?It is also not clear how the authors selected the precursors.Was there a pre-selection process?If yes, which one?How did they conclude that four of each type would be the proper number?
Response: Thanks for the reviewer's comment.We performed an extensive study to evaluate the individual precursor contributions.We added a new section in the Supplementary Material to explain how we selected precursors:

Data Selection
Benefiting from the big data era (using remote sensing and reanalysis data), we can now perform a comprehensive wildfire causation study.We started by collecting all the data listed in Table S1.To make the comparison between the Top-down and Bottom-up groups more objective and transparent, we used a variable clustering method ("varclushi" package in Python) to execute the precursor selection.
Variable clustering is based on the Principal Component Analysis (PCA) algorithm.Firstly, all the input variables are started in one cluster, and a PCA is done for all the variables in this cluster.Secondly, the Eigenvalues (variance explained by each PC for all the variables) of the principal components are checked.The cluster is spilt if the second Eigenvalue is larger than a specified threshold (usually set as 0.7 or 1).This cluster-splitting process is iterated until all the second Eigenvalues in all clusters are lower than the specified threshold and the clusters cannot be further spilt.Thirdly, in each cluster, the representative variables having the highest correlation with their own cluster and the lowest correlation with other clusters are selected according to the formula below: where is the coefficient of determination ( ) of the variable with its own cluster, and the is the of the variable with the nearest cluster.The one with the lowest is selected in each cluster.

Fig S1. Variable clustering result.
We used partial correlation in PCMCI to detect casual relationships, since it can eliminate the interactions between variables.
As a consequence, all variables can be included in the PCMCI analysis.Considering that there are only five bottom-up precursors while seven top-down precursors, we decided to select the top five variables from the top-down group in order to prevent any artificial inflation of the importance of top-down variables.We applied variable clustering for all the 232 ecoregions and recorded the representative variables in each ecoregion.For each variable, we counted globally the pixels where it is representative, the result is shown in Fig S1 .Therefore, according to the number of representative pixels, the variables that are finally selected were AAI, Wind, ET0, VPD, and Tmax in the Top-down group and SWDI, EVI, NDVI, GPP, and FPAR in the Bottom-up group.

(Supplementary Material Line 31-54) GENERAL COMMENT #4:
If I understand correctly, this study has serious conceptual (please see specific comments) and methodological flaws that can decisively affect the results and lead the reader to wrong conclusions.One of the most important conceptual/methodological problems is the names and composition of the precursors' groups.First, there are misclassified precursors.For example, the group of hydrological precursors does not seem to me to be the most suitable, as half of them are not hydrological, but atmospheric (precipitation and FWI).See examples referred to in specific comments.Secondly, and as a consequence of the first, the groups do not seem to me to be the most suitable.For example, in the case of hydrological precursors, it seems to me that it would be more appropriate to consider a group of soil precursors or, possibly, soil moisture precursors.On the one hand, the state and type of vegetation depend more on soil moisture than on all other hydrological aspects.I conclude that the authors agree with this statement because the group of hydrological precursors integrate measurements of soil moisture and not hydrology (e.g., surface and groundwater, with regard to their mechanical, physical and chemical properties, their geographic distribution, etc.).On the other hand, precursors should be independent within each group and between groups, which they clearly are not.The variables are all related to each other in such an obvious way that it is not even necessary to mention an example.

Response:
We appreciate the reviewer's constructive comment, and we agree that such a grouping system could be misleading.In the revised version, we changed the grouping system.Now we categorize precursors into top-down and bottom-up groups, which is a widely used terminology in wildfire studies 1,2  We agree that it would be beneficial to ensure mutually independent.However, this would be a very stringent requirement.To eliminate the influence of interactions, we used partial correlation to detect casual relationships.Partial correlation quantifies the correlation between two variables when conditioned on other variables.After removing the effect of other variables, what remains is the partial correlation between the two target variables.Therefore, we can include all the wildfire precursors in this causation study, even if they are dependent; this is in fact the justification for and advantage of using PCMCI 3 .To maintain a balance between the Top-down group and the Bottomup group, we used variable clustering to find the representative precursors globally.By doing this, we cannot eliminate all interactions among precursors but try to keep them as independent as possible.By using the conceptual grouping system, variable clustering, and partial correlation, the approach allows attributing causation to the correct precursors even if they are interdependent.

Gill L, Taylor AH. Top-Down and Bottom-Up Controls on Fire Regimes Along an Elevational
Gradient on the East Slope of the Sierra Nevada, California, USA.Fire Ecology 2009, 5(3): 57-75.

GENERAL COMMENT #5:
The wildfire research community recognizes and agrees that, for a vegetation wildfire to occur, the following requirements are necessary: (i) fuels/plant biomass, (ii) atmospheric conditions that lead the fuels/vegetation to an adequate state of dryness and, (iii) a source of ignition.The adequate state of dryness of the fuels (live or dead vegetation) is reached with atmospheric conditions of high temperature and low humidity that lead the vegetation to hydric and/or thermal stress for some time (depending on the type and state of the vegetation).Despite this common knowledge, the authors decided to reclassify atmospheric factors into atmospheric and hydrological.They consider: measures of energy (temperature, radiation, and evaporation) and call them atmospheric precursors; and, measures of water availability (precipitation, air, and soil humidity), which are essentially atmospheric and call them hydrological precursors.It is not possible to conclude that the hydrologic precursors are more or less important than the others if the hydrologic precursors are not hydrologic.
The authors also do not describe what might be the limitations and constraints that could affect the conclusions of the study.
Without a major revision of the manuscript to clarify and correct the problems identified in this general appreciation and in the specific comments, it is not worthwhile to analyze other aspects of the manuscript.
Response: Thanks for the reviewer's comment, we changed the grouping system as explained above.In the new system, we classify atmospheric conditions (including energy and humidity) as the topdown group, and fuel/plant conditions as the bottom-up group.
We also discussed the limitations of this study in the Implication part as follows: There are three limitations of this study that could be addressed further.First, although benefiting from the big data era, the number of precursors included in this study is still limited.Major climate modes (e.g., El Niño-Southern Oscillation, and North Atlantic Oscillation) modulate regional top-down and bottom-up conditions and dominant wildfires 69, 70 .Therefore, wildfire prediction could benefit from the achievable predictability of El Niño-Southern Oscillation 71 and other major climate modes.Human behavior (e.g., ignition, cropland expansion, and fire suppression) is an important wildfire impact factor that may alter causal relationships.However, limited by the availability of high temporal resolution climate mode and human behavior indices, we currently cannot conduct such an analysis.Second, a better wildfire precursor grouping system may be beneficial.While we divided the precursors into top-down and bottom-up groups which are conceptually clear 72 , sometimes, the top-down precursors dominate wildfires through their influence on fuel accumulation that is deduced from their long time lags, making the causal inference complex.Third, we assumed the causal relationship to be constant throughout the whole study period.However, extreme events or human behavior can alter fire regimes and potentially change causal relationships.(Line 253-266)

SPECIFIC COMMENT #1:
Line 4: In this manuscript, the authors used the concept of fire (a planned, wanted, and controlled combustion), which I believe is not correct; the authors should use the concept of wildfire (an unplanned and unwanted combustion of dead or alive vegetal biomass in a rural or natural area).

Response:
We appreciate the accuracy of the word/term used by the reviewer and we have replaced fire with wildfire in the revision.

SPECIFIC COMMENT #2:
Line 4: the first sentence is completely vague; what role?What services?How?
Response: We deleted this sentence in the abstract.

SPECIFIC COMMENT #3:
Line 30: Is it really possible to "predict" wildfires "reliably and accurately?
Response: Thanks for the reviewer's comment.We have rephrased this sentence to: Better understanding and management of wildfires, therefore, is necessary to mitigate their negative impacts on human livelihood and the Earth`s natural system.(Line 28-29) SPECIFIC COMMENT #4: Lines 31-32: "Fuel availability, usually in the form of vegetation conditions,…", fuel availability is not equal to vegetation conditions.Please correct the sentence.

Response:
We appreciate the reviewer's careful review.We changed it to: The availability of fuel is highly influenced by previous and current vegetation status and is a precondition for wildfires, yet extensive burning only occurs when fuels are dry and weather conditions are conducive to wildfire spread 18 .(Line 29-31) SPECIFIC COMMENT #5: Line 36: I agree that the FWI system is used widely.However, since it was developed for Canada, it must be calibrated before can be used in any other location.How did you calibrate the FWI indices in order to be able to use them globally?
Response: Thanks for the reviewer's comment, we totally agree that the global use of the FWI system should be done in a careful manner.Its inputs (noon air temperature, air relative humidity, 24-hour accumulated precipitation, and wind speed) have become part of the Top-down group of precursors in the revision, while other direct measurements have been used to characterize bottomup fuel status.To avoid this kind of conceptual problem, we have no longer included the FWI as a precursor in the revised analysis.

SPECIFIC COMMENT #6:
Line 46: I suppose that the black circle character is a typo/not necessary.
Response: Thanks for the comment, we checked the submitted .pdffile and the .pdffile downloaded from the submission system but did not find the black circle characters.We assume this is raised by a different kind or version of the .pdfreader.Apologies for this unclarity.

SPECIFIC COMMENT #7:
Line 46: you mentioned that you use air temperature (Temp).However, you do not explain what air temperature is; the minimum, mean, maximum or other air temperature?Air temperature at what level?
Response: Thanks for the reviewer's comment, we used maximum air temperature in the revised analysis.We listed all the precursors in a Table in the Supplementary Material as we responded in GENERAL COMMENT #2.

SPECIFIC COMMENT #8:
Line 48: can you explain why did you classify Precipitation, which can be defined as the fall of water, in any form (rain, snow, hail, hail, etc.), from the atmosphere to the earth's surface, as a hydrologic and not an atmospheric precursor?
Response: Thanks for the reviewer's comment.In the new grouping system, we classify Precipitation as a top-down precursor.However, the precursor pre-selection (variable clustering) showed its limited representativeness (Fig. S1), and as such precipitation was not included as one of the main precursors.

SPECIFIC COMMENT #9:
Lines 48-49: Please explain why you classify the fire WEATHER index (FWI) as a hydrologic and not an atmospheric precursor.Please note that the Canadian Forest Fire Weather Index (FWI) System consists of six components that account for the effects of fuel moisture and WEATHER conditions on fire behavior, computed with noon air temperature, air relative humidity and wind speed, and 24-hour accumulated precipitation.
Please quantify and explain how these misclassifications impact your findings.
Response: Thanks for the reviewer's comment.We abandoned FWI as it will raise a conceptual problem and we explained the reason above in SPECIFIC COMMENT #5: "Its inputs (noon air temperature, air relative humidity, 24-hour accumulated precipitation, and wind speed) have become part of the Top-down group of precursors in the revision, while other direct measurements have been used to characterize bottom-up fuel status.To avoid this kind of conceptual problem, we have no longer included the FWI as a precursor in the revised analysis." We also discussed the limitations of this study including the impact of the grouping system as we responded in GENERAL COMMENT #5: Second, a better wildfire precursor grouping system may be beneficial.While we divided the precursors into top-down and bottom-up groups, which are conceptually clear 72 , sometimes, the top-down precursors dominate wildfires through their influence on fuel accumulation that is deduced from their long time lags, making the causal inference complex.(Line 260-264) SPECIFIC COMMENT #10: Line 57 (Fig. 1a): Please explain why the sum of the partial correlations of all 12 fire precursors per ecoregion ranges between 0 and 2.
Response: Thanks for the reviewer's question.The partial correlations are actually partial correlation coefficients (R), we summed the absolute value of partial correlation coefficients to find the hotspot regions in the submitted version.Now we realize that the partial coefficient of determination (R 2 ) is easier to understand, so we summed all the partial R 2 , in this case, the sum values should range between 0 and 1.

SPECIFIC COMMENT #11:
Lines 57-61: "For all the 232 ecoregions, the dominant area fraction of the precursor (Fig. S1) shows that VPD (47.6%) is the most important atmospheric precursor followed by Rad, Temp, and ET0 (23.7%, 18.1%, 10.6%, respectively)".What is the meaning of these results?Does this mean that VPD is the most important precursor in 47.6% of the 232 ecoregions, analyzed independently or jointly?If I understand correctly, the sum of the percentages of the four precursors of each type is 100%.Does this mean that you apply PCMCI independently for the precursors of each type/group (for all the ecoregions)?Please clarify.

Response:
We appreciate the careful review.We only applied PCMCI once in each ecoregion and we may get a dominant precursor belonging to either the top-down or bottom-up group.Then we do the comparison in each group based on the dominant pixels.We changed the description as follows: To investigate the dominant wildfire precursors globally, we applied PCMCI to build causal networks for each of the 232 ecoregions (29 climate zones multiplied by 8 vegetation types), using the top-down weather and bottom-up fuel grouping system.Then, in each ecoregion, we may detect a dominant precursor belonging to either the top-down or bottom-up group according to whether there is a causal relationship and the strength of the causal relationship (in this case partial correlation, Fig. 1).(Line 52-56) We ranked the importance (dominant area fraction/pixel number) of the precursors in each group (Fig. S1).In the top-down group, ET0 is the most important precursor followed by VPD, AAI, Tmax, and Wind, accounting for 41.1%, 32.0%, 16.6%, 10.3%, and 0% of top-down dominant regions, respectively.In the bottom-up group, SWDI is the most important precursor followed by GPP,EVI,FPAR,and NDVI,accounting for 56.4%,21.4%,11.1%,8.6%,and 2.5% of  Response: We appreciate the reviewer's careful review.We used the dominant pixel numbers to compute these percentages and changed the description as: Globally, the top-down group shows more dominance than the bottom-up group, accounting for 73.3% and 26.7 % of the globe where causal relationships are detected (Fig. 1c).(Line 59-61) SPECIFIC COMMENT #13: Line 83 and 91: this is only due to the precursors you selected and how you classify them.
Response: Thanks for the reviewer's comment.We believe that our revised top-down and bottomup grouping system addresses this concern, and we revised the relevant statements as follows: Wildfires are positively related to top-down precursors globally (91.6% positive, Fig. 1d).This positive relationship indicates that in flammability-limited regions, BA will increase when more energy is supplied (higher Tmax and ET0) or the environment is drier (higher VPD and AAI).This is supported by evidence indicating that wildfires increase with the increased frequency and intensity of summer heatwaves 25 , the strengthened solar radiation absorption 26, 27 , and the increased atmospheric water demand and drought conditions 28, 29 (see Fig. S2 for ET0, AAI, and VPD  dominance).Major climate modes (e.g., El Niño-Southern Oscillation, and North Atlantic Oscillation) also modulate the occurrence of these weather anomalies 30, 31, 32, 33, 34 and as such play an important role in governing global patterns of wildfire activities 33, 35, 36 .The top-down dominance in the rainforest is supported by studies showing that here wildfires are limited to the time window when fuels are dry enough to burn 37, 38 , see Fig. S2 for the AAI dominance.(Line 86-97) SPECIFIC COMMENT #14: Line 87: another black circle!Response: Thanks for the comment, and apologies for this inconvenience which may be related to different pdf readers.

SPECIFIC COMMENT #15:
Lines 87-89: An obvious and unnecessary sentence.Is there any region where the atmospheric precursors do not play an important role?
Response: Thanks for the reviewer's detailed suggestion to improve the statement of this manuscript.We revised the relevant statement as follows: The top-down dominance in the rainforest is supported by studies showing that here wildfires are limited to the time window when fuels are dry enough to burn 37, 38 , see Fig. S2 for the AAI dominance.(Line 95-97) SPECIFIC COMMENT #16: Lines 91-92: "Fires tend to be negatively related to hydrologic precursors (72.6%), and less frequently this relationship is positive (27.4%), see Fig. 1d"; the part ", and less frequently this relationship is positive (27.4%)," of the sentence is completely unnecessary; the reader can easily compute 100-72.6=27.4.

Response:
Thanks for the reviewer pointing this out.We revised the relevant statement as follows: Wildfires are positively related to top-down precursors globally (91.6% positive, Fig. 1d).(Line 86-

87)
Wildfires tend to be negatively related to bottom-up precursors (78.9% negative, Fig. 1d).(Line 98) SPECIFIC COMMENT #17: Lines 92-94: another obvious and unnecessary sentence; it is obvious that drought and precipitation deficits may provide optimal conditions for wildfires while the opposite, a "high water supply" can prevent fires.
Response: Thanks for the reviewer's comment.We deleted redundant and unnecessary sentences throughout the manuscript.
Response: Thanks for the reviewer's comment.As we changed the grouping system, there is no more hydrologic group and this sentence has been removed from the revised manuscript.We checked the statements about partial correlation relationships (sign) and avoided such ambiguous statements.

SPECIFIC COMMENT #19:
Line 110: I am confused.If "All the data sets are averaged or summed up to weekly" (line 287), how can you have assessed the partial correlation for time lags shorter than 1 week?
Response: Thanks for the reviewer pointing this out and we regret such an inaccurate statement.We revised it as: Longer time lags are shown in the boreal forests, African and Australian savannahs, the Indian Peninsula, the Indochina Peninsula, and southeastern South America (Fig. 2a).In the other regions, wildfires are a direct result of the precursing conditions (mostly no more than 1 week).(Line 111-

113) SPECIFIC COMMENT #20:
Lines 112-113: "…, which means in such regions fires are ignited shortly after (within a few days) the changes in the fire precursors."another obvious and unnecessary sentence.
Response: Thanks for the reviewer's comment.We deleted such redundant sentences in the manuscript and made the statements more concise and meaningful as follows: In the other regions, wildfires are a direct result of the precursing conditions (mostly no more than 1 week).(Line 112-113) The bottom-up precursors can also come with negligible time lags (0 weeks, Fig. 2b), mainly in Australia and South America (Fig. 2c and Fig. S7), indicating that they can serve as not only fuel accumulation (longer time lag) but also fuel dryness Response: Thanks for the question.We cited a paper to support our explanation.A time lag of half a year is likely to follow from a peak in vegetation productivity and thus fuel accumulation in the wet season, which is then followed by fires in the subsequent dry season.
In this case, one possible reason is that the top-down precursors affect wildfires through their influence on fuel accumulation and availability.This is supported by a study conducted by Murray-Tortarolo et al., 2016  4 1 indicating that in dry ecosystems, the dry season precipitation affects annual net primary productivity.(Line 118-121) SPECIFIC COMMENT #22: Lines 125-127: this does not seem correct/accurate; the tropics are a very large region; it is possible to be much more accurate.The explanation does not also seem correct.Please note that 30 weeks is less than a year.
Response: Thanks for pointing this out.We detailed the regions and revised the sentence as follows: The bottom-up precursors show long time lags, especially in Europe, Asia, North America, and Africa (more than half a year, Fig. 2c).One possible reason could be that the current vegetation status or accumulated fuel is related to water supply and vegetation growth in the pre-wildfire seasons 40, 42 .This is supported by studies showing that spring greening related to global warming can either be positively or negatively related to total biomass 43, 44 , through the carryover effect of current vegetation states on subsequent growth caused by increased photosynthesis or earlier soil moisture depletion 39, 40 .(Line 122-127) We cited the "compensation effects of spring warming" related studies, especially reference 44, to support our analysis.In their study, they found that annual spring temperature affects subsequent summer and autumn NDVI.If we take the pre-growing season into account, this carryover effect would last longer from pre-growing to the whole growing season.This is also supported by Murray-Tortarolo et al. (2016).Response: Thanks for the reviewer's careful review.We revised them as follows:

Reference：
To generalize the previous analysis, we evaluated the dominance changes (according to the dominant pixel number) of top-down and bottom-up precursors that are primarily driven by energy,

Minor comments
• The range of the areas of the ecoregions needs to be provided.
Response: Thanks for the reviewer's comment.We added two figures in the Supplementary Material to show the range of the ecoregions according to climate zones and vegetation types:  The vegetation types include sparse vegetation, grassland, cropland, shrubland, tree cover broadleaf evergreen (TBE), tree cover broad-leaf deciduous (TBD), tree cover needle-leaf evergreen (TNE), tree cover needle-leaf deciduous (TND).

Reviewer #2 (Remarks to the Author):
Dear authors, I believe that you have satisfactorily answered the questions raised and proceeded with the adequate revision of the manuscript according to the suggestions provided in the revision report.
I recommend that the manuscript can be accepted for publication.

Response:
We appreciate the reviewer's contributions to improve this manuscript.

Fig 1 .
Fig 1.The sum of the partial coefficient of determination (partial R 2 ) of all 10 wildfire precursors per ecoregion, with higher values meaning higher explained variance of burned area, and vice versa.
bottom-up dominant regions, respectively.(Line 64-69) SPECIFIC COMMENT #12: Lines 72-73: How did you compute these percentages?Did you use the number of ecoregions, the area, or what?
(short time lag) indicators.(Line 127-130) SPECIFIC COMMENT #21: Lines 118-119: what evidence do you have for such a statement?That should not be the reason, at least everywhere.

Mode Climate Mode Index
The majority of climate mode indices are available at a monthly temporal resolution.In our current study, we are interested in the influences of precursors on a weekly temporal resolution, and hence it is not feasible to integrate the climate indices in this framework.A weekly temporal resolution is desirable for our study because fire activity can fluctuate on a daily scale, and much of this information would be lost at a monthly resolution.Although we did not involve climate modes in this study, our results and findings are influential and complementary to earlier work that investigated relationships between climate modes and fire activity.Climate modes act upon wildfires through their influence on the top-down and bottom-up predictors that are here investigated (e.g., precipitation, temperature, GPP).In conclusion, our current precursor study has its own focus.This causal study focuses on what dominates wildfires, where, and the time lags between wildfire precursors and wildfires.For the abovementioned reasons, we believe that the inclusion of the climate modes would distract from the main storyline of this manuscript and would go beyond its scope.
We provide further discussion on relationships between our study and earlier studies focusing on relationships between climate modes and wildfires as suggested by the reviewer in Minor comment #1 and Minor comment #2 (see below).Major comment #3• Similar to the above concern, I think major climate modes (e.g., ENSO and NAO) can be dominant fire precursors, in case these modes are included in the analyses.Response: Thanks for the comment.As we responded in Major comment #2, the inclusion of climate modes will not change the dominance of top-down or bottom-up precursors, and we consider this complementary information.We have appended a discussion on how major climate modes may influence precursors and fire activity, seeMinor comment #1 and Minor comment #2.

.
Le T, Ha KJ, Bae DH.Increasing Causal Effects of El Niño-Southern Oscillation on the Future  Carbon Cycle of Terrestrial Ecosystems.Geophysical Research Letters 2021, 48(24).

Table S1 . All the indices involved in the causal analysis.
The indices marked with '*' were removed from this study according to the data selection result.